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Warianty tytułu
Języki publikacji
Abstrakty
Electricity producers and traders are exposed to various risks, among which price and volume risk play very important roles. This research considers portfolio-building strategies that enable the proportion of electricity traded in different electricity markets (day-ahead and intraday) to be chosen dynamically. Two types of approaches are considered: a simple strategy, which assumes that these proportions are fixed, and a data-driven strategy, in which the ratios fluctuate. To explore the market information, a structural vector autoregressive model is applied, which allows one to estimate the relationship between the variables of interest and simulate their future distribution. The approach is evaluated using data from the electricity market in Germany. The outcomes indicate that data-driven strategies increase revenue and reduce trading risk. These financial gains may encourage energy traders to apply advanced statistical methods in their portfolio-building process.
Czasopismo
Rocznik
Tom
Strony
75--90
Opis fizyczny
Bibliogr. 32 poz., rys.
Twórcy
autor
- Department of Management, Wroclaw University of Science and Technology, Wrocław, Poland
Bibliografia
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- [6] Gianfreda, A., Parisio, L., and Pelagatti, M. The impact of RES in the Italian day-ahead and balancing markets. The Energy Journal 37, Bollino-Madlener Special Issue, (2016), 161–184.
- [7] Gurrib, I., and Elshareif, E. Optimizing the performance of the fractal adaptive moving average strategy: The case of EUR/USD. International Journal of Economics and Finance 8, 2 (2016), 171–179.
- [8] Gurrib, I., and Kamalov, F. The implementation of an adjusted relative strength index model in foreign currency and Energy markets of emerging and development economies. Macroeconomics and Finance in Emerging Market Economies 12, 2 (2019), 105–123.
- [9] Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., and Zareipour, H. Energy forecasting: A review and outlook. IEEE Open Access Journal of Power and Energy 7 (2020), 376–388.
- [10] Janczura, J., and Wójcik, E. Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study. Energy Economics 110 (2022), 106015.
- [11] Kath, C., Nitka, W., Serafin, T., Weron, T., Zaleski, P., and Weron, R. Balancing generation from renewable energy sources: Profitability of an energy trader. Energies 13, 1 (2021), 205.
- [12] Kath, C., and Ziel, F. The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts. Energy Economics 76 (2018), 411–423.
- [13] Ketterer, J. C. The impact of wind power generation on the electricity price in Germany. Energy Economics 44 (2014), 270–280.
- [14] Kiesel, R., and Paraschive, F. Econometric analysis of 15-minute intraday electricity prices. Energy Economics 64 (2017), 77–90.
- [15] Koch, C., and Hirth, L. Short-term electricity trading for system balancing: An empirical analysis of the role of intraday trading in balancing Germany’s electricity system. Renewable and Sustainable Energy Reviews 113, (2019), 109275.
- [16] Koch, C., and Maskos, P. Passive balancing through intraday trading: Whether interactions between short-term trading and balancing stabilize Germany’s electricity systems. International Journal of Energy Economics and Policy 10, 2 (2020), 101–112.
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- [18] Maciejowska, K. Fundamental and speculative shocks, what drives electricity prices? In 11th International Conference on the European Energy Market (EEM14), Krakow, Poland, 2014, IEEE, 2014, pp. 1-5,
- [19] Maciejowska, K. Assessing the impact of renewable energy sources on the electricity price level and variability - a quantile regression approach. Energy Economics 85 (2020), 104532.
- [20] Maciejowska, K., Nitka, W., and Weron, T. Day-ahead vs. intraday – forecasting the price spread to maximize economic benefits. Energies 12, 4 (2019), 631.
- [21] Maciejowska, K., Nitka, W., and Weron, T. Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices. Energy Economics 99, (2021), 105273.
- [22] Maciejowska, K., Uniejewski, B., and Serafin, T. PCA forecast averaging – predicting day-ahead and intraday electricity prices. Energies 13, 14 (2020), 3530.
- [23] Pape, C., Hagemann, S., and Weber, C. Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power markets. Energy Economics 54 (2016), 376–387.
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- [25] Rintamäki, T., Siddiqui, A. S., and Salo, A. Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany. Energy Economics 62 (2017), 270–282.
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- [27] Silva, S., Soares, I., and Pinho, C. The impact of renewable energy sources on economic growth and CO2 emissions – a SVAR approach. European Research Studies Journal 15, 4 (2012), 133–144.
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- [29] Uniejewski, B., Marcjasz, G., and Weron, R. Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO. International Journal of Forecasting 35, 4 (2019), 1533–1547.
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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